Overview

Dataset statistics

Number of variables20
Number of observations176573
Missing cells176543
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.9 MiB
Average record size in memory160.0 B

Variable types

CAT10
NUM10

Warnings

batsman has a high cardinality: 514 distinct values High cardinality
bowler has a high cardinality: 404 distinct values High cardinality
non_striker has a high cardinality: 509 distinct values High cardinality
player_out has a high cardinality: 487 distinct values High cardinality
fielder_caught_out has a high cardinality: 509 distinct values High cardinality
season is highly correlated with idHigh correlation
id is highly correlated with seasonHigh correlation
total_runs is highly correlated with batsman_runsHigh correlation
batsman_runs is highly correlated with total_runsHigh correlation
type_out is highly correlated with replacementsHigh correlation
replacements is highly correlated with type_outHigh correlation
replacements has 176543 (> 99.9%) missing values Missing
extras_noballs is highly skewed (γ1 = 24.59266034) Skewed
extras_byes is highly skewed (γ1 = 29.80374639) Skewed
replacements is uniformly distributed Uniform
extras_wides has 171230 (97.0%) zeros Zeros
extras_legbyes has 173664 (98.4%) zeros Zeros
extras_noballs has 175870 (99.6%) zeros Zeros
extras_byes has 176097 (99.7%) zeros Zeros
total_extras_runs has 167142 (94.7%) zeros Zeros
batsman_runs has 71130 (40.3%) zeros Zeros
total_runs has 62100 (35.2%) zeros Zeros

Reproduction

Analysis started2020-09-24 16:29:20.382580
Analysis finished2020-09-24 16:30:24.462608
Duration1 minute and 4.08 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct746
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean713160.0962
Minimum335982
Maximum1178425
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-09-24T22:00:24.649699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum335982
5-th percentile336019
Q1501208
median598047
Q3980985
95-th percentile1175368
Maximum1178425
Range842443
Interquartile range (IQR)479777

Descriptive statistics

Standard deviation284366.5362
Coefficient of variation (CV)0.3987415135
Kurtosis-1.33600124
Mean713160.0962
Median Absolute Deviation (MAD)205835
Skewness0.3585860896
Sum1.259248177e+11
Variance8.086432689e+10
MonotocityNot monotonic
2020-09-24T22:00:24.910779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8297372620.1%
 
8298112590.1%
 
5012212570.1%
 
7340472570.1%
 
11784232570.1%
 
4191422570.1%
 
5483672560.1%
 
3921902560.1%
 
8298052560.1%
 
5483532550.1%
 
Other values (736)17400198.5%
 
ValueCountFrequency (%) 
3359822250.1%
 
3359832480.1%
 
3359842190.1%
 
3359852460.1%
 
3359862400.1%
 
ValueCountFrequency (%) 
11784252230.1%
 
117842451< 0.1%
 
11784232570.1%
 
11784222460.1%
 
11784212420.1%
 

season
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.368386
Minimum2008
Maximum2019
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-09-24T22:00:25.187634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12011
median2013
Q32016
95-th percentile2019
Maximum2019
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.323319105
Coefficient of variation (CV)0.001650626447
Kurtosis-1.126435923
Mean2013.368386
Median Absolute Deviation (MAD)3
Skewness0.07451207835
Sum355506496
Variance11.04444988
MonotocityIncreasing
2020-09-24T22:00:25.438641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
20131815210.3%
 
20121776710.1%
 
2011170139.6%
 
2010144898.2%
 
2014142888.1%
 
2018142868.1%
 
2016140968.0%
 
2017138497.8%
 
2015136417.7%
 
2009135957.7%
 
Other values (2)2539714.4%
 
ValueCountFrequency (%) 
2008134897.6%
 
2009135957.7%
 
2010144898.2%
 
2011170139.6%
 
20121776710.1%
 
ValueCountFrequency (%) 
2019119086.7%
 
2018142868.1%
 
2017138497.8%
 
2016140968.0%
 
2015136417.7%
 

batsman
Categorical

HIGH CARDINALITY

Distinct514
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
V Kohli
 
4202
SK Raina
 
3968
S Dhawan
 
3732
RG Sharma
 
3732
G Gambhir
 
3524
Other values (509)
157415 
ValueCountFrequency (%) 
V Kohli42022.4%
 
SK Raina39682.2%
 
S Dhawan37322.1%
 
RG Sharma37322.1%
 
G Gambhir35242.0%
 
RV Uthappa34221.9%
 
DA Warner33971.9%
 
MS Dhoni32601.8%
 
AM Rahane32081.8%
 
CH Gayle30731.7%
 
Other values (504)14105579.9%
 
2020-09-24T22:00:25.762275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique13 ?
Unique (%)< 0.1%
2020-09-24T22:00:26.064531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length9
Mean length9.347589949
Min length5

bowler
Categorical

HIGH CARDINALITY

Distinct404
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Harbhajan Singh
 
3352
PP Chawla
 
3133
A Mishra
 
3100
R Ashwin
 
2966
SL Malinga
 
2878
Other values (399)
161144 
ValueCountFrequency (%) 
Harbhajan Singh33521.9%
 
PP Chawla31331.8%
 
A Mishra31001.8%
 
R Ashwin29661.7%
 
SL Malinga28781.6%
 
P Kumar26371.5%
 
B Kumar26311.5%
 
DJ Bravo26201.5%
 
UT Yadav25711.5%
 
SP Narine25451.4%
 
Other values (394)14814083.9%
 
2020-09-24T22:00:26.470526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-24T22:00:27.029323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length9
Mean length9.535931315
Min length5

innings
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1st
91487 
2nd
85086 
ValueCountFrequency (%) 
1st9148751.8%
 
2nd8508648.2%
 
2020-09-24T22:00:27.295545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-24T22:00:27.471091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:27.630523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

non_striker
Categorical

HIGH CARDINALITY

Distinct509
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
SK Raina
 
4092
V Kohli
 
4061
S Dhawan
 
4034
RG Sharma
 
3771
G Gambhir
 
3740
Other values (504)
156875 
ValueCountFrequency (%) 
SK Raina40922.3%
 
V Kohli40612.3%
 
S Dhawan40342.3%
 
RG Sharma37712.1%
 
G Gambhir37402.1%
 
AM Rahane34572.0%
 
RV Uthappa33271.9%
 
DA Warner31261.8%
 
AB de Villiers29821.7%
 
CH Gayle29691.7%
 
Other values (499)14101479.9%
 
2020-09-24T22:00:27.960206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)< 0.1%
2020-09-24T22:00:28.431050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length9
Mean length9.352426475
Min length5

replacements
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct30
Distinct (%)100.0%
Missing176543
Missing (%)> 99.9%
Memory size1.3 MiB
{'role': [{'in': 'CJ Anderson', 'reason': 'injury', 'role': 'bowler'}]}
 
1
{'role': [{'in': 'Harbhajan Singh', 'out': 'DL Chahar', 'reason': 'injury', 'role': 'bowler'}]}
 
1
{'role': [{'in': 'JM Kemp', 'reason': 'excluded - high full pitched balls', 'role': 'bowler'}]}
 
1
{'role': [{'in': 'TM Head', 'reason': 'injury', 'role': 'bowler'}]}
 
1
{'role': [{'in': 'AT Rayudu', 'out': 'SR Tendulkar', 'reason': 'injury', 'role': 'batter'}]}
 
1
Other values (25)
25 
ValueCountFrequency (%) 
{'role': [{'in': 'CJ Anderson', 'reason': 'injury', 'role': 'bowler'}]}1< 0.1%
 
{'role': [{'in': 'Harbhajan Singh', 'out': 'DL Chahar', 'reason': 'injury', 'role': 'bowler'}]}1< 0.1%
 
{'role': [{'in': 'JM Kemp', 'reason': 'excluded - high full pitched balls', 'role': 'bowler'}]}1< 0.1%
 
{'role': [{'in': 'TM Head', 'reason': 'injury', 'role': 'bowler'}]}1< 0.1%
 
{'role': [{'in': 'AT Rayudu', 'out': 'SR Tendulkar', 'reason': 'injury', 'role': 'batter'}]}1< 0.1%
 
{'role': [{'in': 'Bipul Sharma', 'out': 'Harmeet Singh', 'reason': 'excluded - high full pitched balls', 'role': 'bowler'}]}1< 0.1%
 
{'role': [{'in': 'MP Stoinis', 'out': 'Mohammed Siraj', 'reason': 'excluded - high full pitched balls', 'role': 'bowler'}]}1< 0.1%
 
{'role': [{'in': 'DL Chahar', 'out': 'KM Jadhav', 'reason': 'injury', 'role': 'batter'}]}1< 0.1%
 
{'role': [{'in': 'Yuvraj Singh', 'out': 'KC Sangakkara', 'reason': 'injury', 'role': 'batter'}]}1< 0.1%
 
{'role': [{'in': 'R Ashwin', 'out': 'Mujeeb Ur Rahman', 'reason': 'injury', 'role': 'bowler'}]}1< 0.1%
 
Other values (20)20< 0.1%
 
(Missing)176543> 99.9%
 
2020-09-24T22:00:28.835544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique30 ?
Unique (%)100.0%
2020-09-24T22:00:29.169684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length124
Median length3
Mean length3.014424629
Min length3

bowled_over
Real number (ℝ≥0)

Distinct180
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.528801685
Minimum0.1
Maximum19.9
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-09-24T22:00:29.589790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q14.5
median9.4
Q314.4
95-th percentile18.5
Maximum19.9
Range19.8
Interquartile range (IQR)9.9

Descriptive statistics

Standard deviation5.677219708
Coefficient of variation (CV)0.5957957669
Kurtosis-1.180961644
Mean9.528801685
Median Absolute Deviation (MAD)4.9
Skewness0.04965397921
Sum1682529.1
Variance32.23082361
MonotocityNot monotonic
2020-09-24T22:00:29.944702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.114910.8%
 
0.514900.8%
 
0.114900.8%
 
0.314900.8%
 
2.114900.8%
 
3.114900.8%
 
0.614900.8%
 
0.214900.8%
 
0.414900.8%
 
4.114890.8%
 
Other values (170)16167391.6%
 
ValueCountFrequency (%) 
0.114900.8%
 
0.214900.8%
 
0.314900.8%
 
0.414900.8%
 
0.514900.8%
 
ValueCountFrequency (%) 
19.95< 0.1%
 
19.835< 0.1%
 
19.72390.1%
 
19.69690.5%
 
19.510170.6%
 

batsman_team
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Mumbai Indians
22149 
Royal Challengers Bangalore
20770 
Kings XI Punjab
20684 
Kolkata Knight Riders
20592 
Chennai Super Kings
19271 
Other values (10)
73107 
ValueCountFrequency (%) 
Mumbai Indians2214912.5%
 
Royal Challengers Bangalore2077011.8%
 
Kings XI Punjab2068411.7%
 
Kolkata Knight Riders2059211.7%
 
Chennai Super Kings1927110.9%
 
Delhi Daredevils1878010.6%
 
Rajasthan Royals171479.7%
 
Sunrisers Hyderabad125257.1%
 
Deccan Chargers90345.1%
 
Pune Warriors54433.1%
 
Other values (5)101785.8%
 
2020-09-24T22:00:30.294998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-24T22:00:30.670259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length16
Mean length17.99051384
Min length13

player_out
Categorical

HIGH CARDINALITY

Distinct487
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
167862 
SK Raina
 
157
RG Sharma
 
152
RV Uthappa
 
151
V Kohli
 
142
Other values (482)
 
8109
ValueCountFrequency (%) 
016786295.1%
 
SK Raina1570.1%
 
RG Sharma1520.1%
 
RV Uthappa1510.1%
 
V Kohli1420.1%
 
G Gambhir1360.1%
 
S Dhawan1350.1%
 
KD Karthik1340.1%
 
PA Patel1250.1%
 
AM Rahane1150.1%
 
Other values (477)74644.2%
 
2020-09-24T22:00:31.033092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique84 ?
Unique (%)< 0.1%
2020-09-24T22:00:31.362166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length1
Mean length1.413789198
Min length1

fielder_caught_out
Categorical

HIGH CARDINALITY

Distinct509
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
170319 
MS Dhoni
 
152
KD Karthik
 
151
RV Uthappa
 
123
AB de Villiers
 
113
Other values (504)
 
5715
ValueCountFrequency (%) 
017031996.5%
 
MS Dhoni1520.1%
 
KD Karthik1510.1%
 
RV Uthappa1230.1%
 
AB de Villiers1130.1%
 
SK Raina1100.1%
 
PA Patel950.1%
 
RG Sharma900.1%
 
V Kohli86< 0.1%
 
NV Ojha82< 0.1%
 
Other values (499)52523.0%
 
2020-09-24T22:00:31.710886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique97 ?
Unique (%)0.1%
2020-09-24T22:00:32.027173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length1
Mean length1.302135661
Min length1

type_out
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
167862 
caught
 
5219
bowled
 
1566
run out
 
844
lbw
 
530
Other values (5)
 
552
ValueCountFrequency (%) 
016786295.1%
 
caught52193.0%
 
bowled15660.9%
 
run out8440.5%
 
lbw5300.3%
 
stumped2800.2%
 
caught and bowled2500.1%
 
retired hurt11< 0.1%
 
hit wicket10< 0.1%
 
obstructing the field1< 0.1%
 
2020-09-24T22:00:32.310174image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-24T22:00:32.651375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:32.969412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length1
Mean length1.260288946
Min length1

extras_wides
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03682329688
Minimum0
Maximum5
Zeros171230
Zeros (%)97.0%
Memory size1.3 MiB
2020-09-24T22:00:33.233424image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2516125522
Coefficient of variation (CV)6.832971881
Kurtosis191.5014881
Mean0.03682329688
Median Absolute Deviation (MAD)0
Skewness11.65890837
Sum6502
Variance0.0633088764
MonotocityNot monotonic
2020-09-24T22:00:33.460969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
017123097.0%
 
148582.8%
 
22290.1%
 
52070.1%
 
345< 0.1%
 
44< 0.1%
 
ValueCountFrequency (%) 
017123097.0%
 
148582.8%
 
22290.1%
 
345< 0.1%
 
44< 0.1%
 
ValueCountFrequency (%) 
52070.1%
 
44< 0.1%
 
345< 0.1%
 
22290.1%
 
148582.8%
 

extras_legbyes
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02119803141
Minimum0
Maximum5
Zeros173664
Zeros (%)98.4%
Memory size1.3 MiB
2020-09-24T22:00:33.724102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1949347214
Coefficient of variation (CV)9.195887942
Kurtosis241.5230135
Mean0.02119803141
Median Absolute Deviation (MAD)0
Skewness13.74586003
Sum3743
Variance0.03799954562
MonotocityNot monotonic
2020-09-24T22:00:33.995092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
017366498.4%
 
125361.4%
 
42160.1%
 
21360.1%
 
317< 0.1%
 
54< 0.1%
 
ValueCountFrequency (%) 
017366498.4%
 
125361.4%
 
21360.1%
 
317< 0.1%
 
42160.1%
 
ValueCountFrequency (%) 
54< 0.1%
 
42160.1%
 
317< 0.1%
 
21360.1%
 
125361.4%
 

extras_noballs
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004179574454
Minimum0
Maximum5
Zeros175870
Zeros (%)99.6%
Memory size1.3 MiB
2020-09-24T22:00:34.197825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.07055253733
Coefficient of variation (CV)16.88031595
Kurtosis1056.80134
Mean0.004179574454
Median Absolute Deviation (MAD)0
Skewness24.59266034
Sum738
Variance0.004977660524
MonotocityNot monotonic
2020-09-24T22:00:34.418641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
017587099.6%
 
16870.4%
 
29< 0.1%
 
56< 0.1%
 
31< 0.1%
 
ValueCountFrequency (%) 
017587099.6%
 
16870.4%
 
29< 0.1%
 
31< 0.1%
 
56< 0.1%
 
ValueCountFrequency (%) 
56< 0.1%
 
31< 0.1%
 
29< 0.1%
 
16870.4%
 
017587099.6%
 

extras_byes
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004961120896
Minimum0
Maximum4
Zeros176097
Zeros (%)99.7%
Memory size1.3 MiB
2020-09-24T22:00:34.619147image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1166742622
Coefficient of variation (CV)23.51772203
Kurtosis971.0286256
Mean0.004961120896
Median Absolute Deviation (MAD)0
Skewness29.80374639
Sum876
Variance0.01361288346
MonotocityNot monotonic
2020-09-24T22:00:34.837416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
017609799.7%
 
13210.2%
 
41210.1%
 
231< 0.1%
 
33< 0.1%
 
ValueCountFrequency (%) 
017609799.7%
 
13210.2%
 
231< 0.1%
 
33< 0.1%
 
41210.1%
 
ValueCountFrequency (%) 
41210.1%
 
33< 0.1%
 
231< 0.1%
 
13210.2%
 
017609799.7%
 

extras_penalty
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
176571 
5
 
2
ValueCountFrequency (%) 
0176571> 99.9%
 
52< 0.1%
 
2020-09-24T22:00:35.145487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-24T22:00:35.302660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:35.454681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

total_extras_runs
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06721865744
Minimum0
Maximum7
Zeros167142
Zeros (%)94.7%
Memory size1.3 MiB
2020-09-24T22:00:35.654572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3430137251
Coefficient of variation (CV)5.102954122
Kurtosis91.09801952
Mean0.06721865744
Median Absolute Deviation (MAD)0
Skewness8.226940401
Sum11869
Variance0.1176584156
MonotocityNot monotonic
2020-09-24T22:00:35.871450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
016714294.7%
 
184014.8%
 
24040.2%
 
43420.2%
 
52180.1%
 
365< 0.1%
 
71< 0.1%
 
ValueCountFrequency (%) 
016714294.7%
 
184014.8%
 
24040.2%
 
365< 0.1%
 
43420.2%
 
ValueCountFrequency (%) 
71< 0.1%
 
52180.1%
 
43420.2%
 
365< 0.1%
 
24040.2%
 

batsman_runs
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.237431544
Minimum0
Maximum6
Zeros71130
Zeros (%)40.3%
Memory size1.3 MiB
2020-09-24T22:00:36.086936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.609116193
Coefficient of variation (CV)1.300367847
Kurtosis1.638279999
Mean1.237431544
Median Absolute Deviation (MAD)1
Skewness1.585889292
Sum218497
Variance2.589254921
MonotocityNot monotonic
2020-09-24T22:00:36.296218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
07113040.3%
 
16541637.0%
 
42007511.4%
 
2112926.4%
 
680354.6%
 
35690.3%
 
556< 0.1%
 
ValueCountFrequency (%) 
07113040.3%
 
16541637.0%
 
2112926.4%
 
35690.3%
 
42007511.4%
 
ValueCountFrequency (%) 
680354.6%
 
556< 0.1%
 
42007511.4%
 
35690.3%
 
2112926.4%
 

total_runs
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.304650201
Minimum0
Maximum7
Zeros62100
Zeros (%)35.2%
Memory size1.3 MiB
2020-09-24T22:00:36.584013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.597156266
Coefficient of variation (CV)1.224202675
Kurtosis1.579171701
Mean1.304650201
Median Absolute Deviation (MAD)1
Skewness1.555697515
Sum230366
Variance2.550908138
MonotocityNot monotonic
2020-09-24T22:00:36.773157image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
17318041.4%
 
06210035.2%
 
42033711.5%
 
2118946.7%
 
679884.5%
 
36720.4%
 
53540.2%
 
748< 0.1%
 
ValueCountFrequency (%) 
06210035.2%
 
17318041.4%
 
2118946.7%
 
36720.4%
 
42033711.5%
 
ValueCountFrequency (%) 
748< 0.1%
 
679884.5%
 
53540.2%
 
42033711.5%
 
36720.4%
 

Interactions

2020-09-24T21:59:39.710107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:40.072531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:40.506458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:40.899890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:41.305315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:41.798201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:42.163316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:42.523097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:43.006752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:43.390873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:43.807631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:44.291962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:44.713405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:45.072555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:45.447040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:45.797760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:46.188961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:46.583988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:47.039521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:47.414936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:47.822871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:48.238416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:48.646300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:49.056341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:49.430008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:49.803783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:50.205786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:50.617060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:51.089232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:51.546268image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:51.931417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:52.295636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:52.646900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:53.031133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:53.381537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:53.771581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:54.122208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:54.491581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:55.432541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:55.794631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:56.214747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:56.628773image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:57.005711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:57.422979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:57.763757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:58.114537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:58.472055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:58.871886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:59.254484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:59.615233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T21:59:59.977989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:00.352701image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:00.729943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:01.096696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:01.487448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:01.898256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:02.255528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:02.613191image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:02.972036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:03.355585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:03.753692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:04.164229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:04.606710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:05.053077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:05.429370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:05.820594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:06.182794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:06.612866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:07.014500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:07.404910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:07.811601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:08.195912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:08.572047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:08.978482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:09.507222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:09.878497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:10.248592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:10.655128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:11.054971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:11.463965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:11.897540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:12.288681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:12.678949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:13.070489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:13.462355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:13.847100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:14.196234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:14.604539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:15.013160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:15.394668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:15.786855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:16.203695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:16.578896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:17.013926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:17.393509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:17.801651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:18.169661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:18.583916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:19.116827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:19.533721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-24T22:00:36.928435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-24T22:00:37.313823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-24T22:00:37.794932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-24T22:00:38.337383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-24T22:00:39.127215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-24T22:00:20.796930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:22.345819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-24T22:00:23.835913image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

idseasonbatsmanbowlerinningsnon_strikerreplacementsbowled_overbatsman_teamplayer_outfielder_caught_outtype_outextras_widesextras_legbyesextras_noballsextras_byesextras_penaltytotal_extras_runsbatsman_runstotal_runs
03359882008AC GilchristGD McGrath1stJC ButtlerNaN0.1Rajasthan Royals00001000101
13359882008AC GilchristGD McGrath1stAM RahaneNaN0.2Rajasthan Royals00000000000
23359882008AC GilchristGD McGrath1stAM RahaneNaN0.3Rajasthan Royals00000000044
33359882008Y Venugopal RaoGD McGrath1stAM RahaneNaN0.4Rajasthan Royals00000000000
43359882008Y Venugopal RaoGD McGrath1stAM RahaneNaN0.5Rajasthan Royals00000000066
53359882008Y Venugopal RaoGD McGrath1stAM RahaneNaN0.6Rajasthan Royals00000000000
63359882008AC GilchristMohammad Asif1stJC ButtlerNaN1.1Rajasthan Royals00000000000
73359882008AC GilchristMohammad Asif1stJC ButtlerNaN1.2Rajasthan Royals00000000000
83359882008AC GilchristMohammad Asif1stJC ButtlerNaN1.3Rajasthan Royals00000000044
93359882008AC GilchristMohammad Asif1stJC ButtlerNaN1.4Rajasthan Royals00000000044

Last rows

idseasonbatsmanbowlerinningsnon_strikerreplacementsbowled_overbatsman_teamplayer_outfielder_caught_outtype_outextras_widesextras_legbyesextras_noballsextras_byesextras_penaltytotal_extras_runsbatsman_runstotal_runs
17656311784242019LS LivingstoneNA Saini2ndMS DhoniNaN18.3Chennai Super Kings00000000011
17656411784242019LS LivingstoneNA Saini2ndSW BillingsNaN18.4Chennai Super Kings00000000000
17656511784242019SV SamsonK Khejroliya2ndSW BillingsNaN18.5Chennai Super Kings00000000022
17656611784242019SV SamsonK Khejroliya2ndSW BillingsNaN18.6Chennai Super Kings00000000044
17656711784242019LS LivingstoneK Khejroliya2ndMS DhoniNaN19.1Chennai Super Kings00000000044
17656811784242019SV SamsonK Khejroliya2ndMS DhoniNaN19.2Chennai Super Kings00000000044
17656911784242019SV SamsonK Khejroliya2ndMS DhoniNaN19.3Chennai Super Kings00000000022
17657011784242019SV SamsonK Khejroliya2ndMS DhoniNaN19.4Chennai Super KingsSW Billings0run out00000000
17657111784242019LS LivingstoneYS Chahal2ndMS DhoniNaN19.5Chennai Super Kings00000000011
17657211784242019SV SamsonYS Chahal2ndDJ BravoNaN19.6Chennai Super Kings00000000011